Signal processors for matching signal patterns such as speech signal patterns or visual signal patterns are known. For example, U.S. Pat. No. 4,181,821, issued Jan. 1, 1980 entitled "Multiple Template Speech Recognition System," discloses a processor for matching, or recognizing, an unknown speech signal as one of a set of reference signals. In many known speech recognition systems, an input utterance is analyzed to provide a set of feature signals which characterize the utterance. Such feature signals may typically be derived from spectral analysis or linear prediction analysis of the utterance. Initially, the recognition system apparatus is trained through the use of input utterances of identified reference words. Each input utterance of a known, identified word is analyzed to provide a set of reference feature signals, which reference signals are stored. Subsequent to the storage of a set of reference signals for each reference word to be recognized by the system, an unknown utterance is analyzed and a set of feature signals representative of the unknown utterance is obtained. The unknown utterance feature signals are compared with the sets of stored reference feature signals to determine the degree of dissimilarity or similarity therebetween. After the comparisons are completed, the unknown utterance is identified as the reference word whose feature signals most closely correspond to the feature signals of the unknown utterance.
As to a signal comparison technique, dynamic programming is a technique which is known for solving various optimization problems. Illustratively, dynamic programming can be used for dynamically time warping, e.g., one signal may be non-linearly stretched or compressed in either time or space to optimally match another signal. See, for example, L. R. Rabiner et al, "Considerations in Dynamic Time Warping Algorithms for Discrete Word Recognition," IEEE Transactions on Acoustics, Speech and Signal Processing, Vol. ASSP-26, No. 6, (December 1978), pp. 575-582, which discloses the use of dynamic time warping for comparing signals in a time domain signal recognition arrangement. Also see, for example, Y. Nakano et al, "Improvement of Chinese Character Recognition Using Projection Profiles," Proceedings of the First International Joint Conference on Pattern Recognition, (Oct. 30-Nov. 1, 1973), pp. 172-178, which discloses the use of dynamic time warping for comparing signals in a space domain signal recognition arrangement.
Special purpose signal processors, which have an architecture for allowing more efficient solution of dynamic programming problems through the use of parallel processing, are also known. For example, the publication by L. J. Guibas, et al, "Direct VLSI Implementation of Combinatorial Algorithms", Proceedings of the CalTech Conference on Very Large Scale Integration, (January 1979) pp. 509-525 discloses an architecture for solving a class of dynamic programming problems, which class may be characterized as the class of parenthesization problems. The Guibas signal processor includes a rectangular array of processor cells. Each cell is connected only to its nearest neighboring cells (i.e., to its nearest upward cell and to its nearest rightward cell). Also, each cell is structurally identical to each and every other cell. Too, data and control information are communicated between neighboring cells in a regular fashion. More particularly, dynamic programming data are synchronously extended in a left-to-right as well as in a bottom-to-top direction by means of two virtual data channels called respectively the fast belt and the slow belt. In addition to so extending data, control information is also extended in a generally left-to-right and bottom-to-top direction by means of a virtual control channel. The two data channels and the control channel share, in time, a single, i.e., actual, communication line which interconnects the neighboring cells. Thereby each of the actual (versus virtual) interprocessor communication lines passes two data signals and one control information signal per unit time.
This known combination of data and control information flow, jointly with the rectangular array of processor cells and a specially designed processor cell, comprises a structure capable of efficiently solving that class of dynamic programming problems characterized as the class of parenthesization problems. Unfortunately, in a diffferent class of dynamic programming problems, e.g., dynamic time warping problems, which class may be characterized as the class of pattern matching problems such as may occur in a speech signal or visual signal recognition system, the aforedescribed known structure does not appear to be satisfactory.